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ORIGINAL ARTICLE
Long-term trends of inequalities in mortality in 6 Europeancountries
Rianne de Gelder . Gwenn Menvielle . Giuseppe Costa .
Katalin Kovacs . Pekka Martikainen . Bjørn Heine Strand .
Johan P. Mackenbach
Received: 29 April 2016 / Revised: 1 November 2016 / Accepted: 10 November 2016 / Published online: 9 December 2016
� The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract
Objectives We aimed to assess whether trends in
inequalities in mortality during the period 1970–2010 dif-
fered between Finland, Norway, England and Wales,
France, Italy (Turin) and Hungary.
Methods Total and cause-specific mortality data by edu-
cational level and, if available, occupational class were
collected and harmonized. Both relative and absolute
measures of inequality in mortality were calculated.
Results In all countries except Hungary, all-cause mortal-
ity declined strongly over time in all socioeconomic
groups. Relative inequalities in all-cause mortality gener-
ally increased, but more so in Hungary and Norway than
elsewhere. Absolute inequalities often narrowed, but went
up in Hungary and Norway. As a result of these trends,
Hungary (where inequalities in mortality where almost
absent in the 1970s) and Norway (where inequalities in the
1970s were among the smallest of the six countries in this
study) now have larger inequalities in mortality than the
other four countries.
Conclusions While some countries have experienced dra-
matic setbacks, others have made substantial progress in
reducing inequalities in mortality.
Keywords Mortality � Socioeconomic inequalities �Trends � Europe
Introduction
Widening relative and/or absolute inequalities in mortality
over the past two decades have been reported from many
countries (Borrell et al. 2008; Fawcett et al. 2005; Jemal
et al. 2008; Krieger et al. 2008; Mackenbach et al. 2003;
Mackenbach et al. 2014; Martikainen et al. 2014; Tarki-
ainen et al. 2012; Strand et al. 2010, 2014), but studies of
long-term trends stretching over three or more decades are
rare, and are usually limited to a single country. Examples
include studies of long-term trends in the United States
(Krieger et al. 2008), Finland (Martikainen et al. 2014),
Norway (Strand et al. 2014), France (Menvielle et al.
2007), England and Wales (Capewell and Graham 2010)
and Italy (Stringhini et al. 2015).
While short-term trends are important for monitoring,
for example because they reflect the effect of changes in
exposure to determinants of mortality with a relatively
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00038-016-0922-9) contains supplementarymaterial, which is available to authorized users.
R. de Gelder � J. P. Mackenbach (&)
Department of Public Health, Erasmus MC, Rotterdam,
The Netherlands
e-mail: [email protected]
G. Menvielle
UPMC Univ Paris 06, INSERM, Institut Pierre Louis
d’Epidemiologie et de Sante Publique (IPLESP UMRS 1136),
Sorbonne Universites, Paris, France
G. Costa
Department of Clinical Medicine and Biology,
University of Turin, Turin, Italy
K. Kovacs
Demographic Research Institute, Budapest, Hungary
P. Martikainen
Department of Sociology, University of Helsinki, Helsinki,
Finland
B. H. Strand
Division of Epidemiology, Norwegian Institute of Public Health,
Oslo, Norway
Int J Public Health (2017) 62:127–141
DOI 10.1007/s00038-016-0922-9
123
immediate impact, such as improvements in medical
treatment or road safety, long-term trends may provide
insights into how secular changes in mortality and its
determinants play out in the evolving pattern of health
inequalities. For example, high-income countries are in an
advanced stage of the epidemiologic transition (Olshansky
and Ault 1986; Omran 1971), with rapidly but differen-
tially declining rates of cardiovascular disease mortality
and widening inequalities in cardiovascular disease mor-
tality as a result (Avendano et al. 2006; Kunst et al. 1999;
Marmot and McDowall 1986). Such secular changes can
only be captured on a time-scale of three or four decades.
Previous studies have provided a mixed picture of long-
term trends in inequalities in mortality. Relative inequali-
ties (e.g., inequalities expressed in terms of a rate ratio,
indicating the strength of the association between socioe-
conomic position and mortality regardless of the absolute
level of mortality) seem to have universally widened, even
in the highly developed welfare states of Western Europe,
but for absolute inequalities (e.g., inequalities expressed in
terms of a rate difference, indicating the absolute mortality
excess in lower as compared to higher socioeconomic
groups) both widening and narrowing have been reported
(Martikainen et al. 2014; Strand et al. 2014; Shkolnikov
et al. 2012; Stringhini et al. 2015; Menvielle et al. 2007).
Because almost no studies have quantitatively compared
these trends between countries, it is unknown whether
countries differ in the timing of widening or narrowing of
inequalities in mortality.
We therefore analyzed trends in socioeconomic
inequalities in mortality over four decades, using a unique
dataset with harmonized data from six European countries.
In addition to the Western European countries mentioned
above, this dataset also covers Hungary, thereby providing
a first analysis of long-term trends in inequalities in mor-
tality in Central/Eastern Europe—a part of the subcontinent
whose political history had a profound impact on mortality
and life expectancy (Mackenbach 2013).
Methods
Data
In this study mortality data were used from Finland, Norway,
England and Wales, France, Italy and Hungary, based on a
total of 269,550,158 person years, covering the period
1970–2010. Key characteristics of the data are shown in
Table 1. Data were harmonized to enhance between- and
within-country comparability and contained information on
sex, age, educational level, occupational class, and cause-
specific mortality. The educational distribution of each
country’s population is presented in web appendix Table 1.
Most data sets covered the entire national territories, but in
Italy data from the Turin region only were available. Previous
studies have shown that patterns and trends observed in this
regional population correspond well to those seen at the
national level (Federico et al. 2013; Marinacci et al. 2013).
Data from Finland, Norway, England and Wales, France
and Italy (Turin) were generated in a longitudinal mortality
follow-up after a census, in which mortality data were linked
to socioeconomic information that had been recorded in the
census; each decade of follow-up was divided in approxi-
mate 5-year periods for the analysis to allow a more fine-
grained analysis of time-trends. Hungary has so-called cross-
sectional unlinked data in which socioeconomic information
on the population-at-risk comes from the census, and on the
deceased comes from the death certificate.
We used two indicators of socioeconomic position: level
of education and occupational class. Education was classi-
fied according to the International Standard Classification
of Education 1997 (UNESCO 2006). Three groups were
distinguished: ‘primary education and lower secondary
education (ISCED 0, 1 and 2; ‘low’)’, ‘upper secondary
education and post-secondary, non-tertiary education
(ISCED 3 and 4; ‘middle’)’, and tertiary education (ISCED
5 and 6; ‘high’). In the datasets for 1981–1991 and
1991–2001 in England and Wales only two levels of edu-
cation could be distinguished (‘low and middle’ vs ‘high’),
and we therefore focussed on the periods 1971–1981 and
2001–2009 for the main analysis. Occupational class was
classified following the Erikson–Goldthorpe–Portocarero
(EGP) social class scheme (Erikson and Goldthorpe 1992).
Four categories were distinguished: non-manual workers,
manual workers, farmers and self-employed, but because
the relative position of farmers and self-employed workers
in the social hierarchy is ambiguous, and information on
occupational class is less reliable for women, we only report
inequalities between men in manual and non-manual
occupations. Because data on education were available for
all countries, whereas data on occupation were available for
men in 4 countries only, inequalities by educational group
are presented as main outcome.
Deaths were classified according to the 8th, 9th or 10th
revision of the International Classification of Diseases
(ICD). ICD-codes were harmonized following the
scheme presented in web appendix Table 2. Our main out-
come variables were all-cause mortality, and mortality due
to cardiovascular disease (ICD-10 I00-I99), cancer (ICD-10
C00-D48), all other diseases (ICD-10 A00-B99, D5-H95
and J00-U85), and external causes (i.e., injuries; V01-Y98).
Analysis
Age-standardized mortality rates by educational level, sex,
country and time-period were calculated using the
128 R. de Gelder et al.
123
European Standard Population (Ahmad et al. 2001).
Analyses were restricted to persons aged 35–79 for anal-
yses by educational level (40–79 in Norway), and to
persons aged 35–64 (active working population) for anal-
yses by occupational class. Ages refer to age at death.
Educational inequalities in mortality were assessed with
both relative and absolute measures, using the Relative
Index of Inequality (RII) and the Slope Index of Inequality
(SII) (Mackenbach and Kunst 1997; Mackenbach et al.
2008). The RII and SII are regression-based measures
which take into account the distribution of education in a
population, and adjust the relative position of each group to
its share in the population, which increases comparability
over time and between countries if there are substantial
changes or differences in distribution of the population
over socioeconomic groups. RIIs were calculated with
Poisson regression with educational ‘rank’ as an indepen-
dent variable, controlling for age (in 5-year age groups).
Educational ‘rank’ was calculated for each education group
(by country, sex and period) as the mean proportion of the
population having a higher level of education. This ensures
that all education groups (not only the lowest and highest)
are taken into account, and that the magnitude of
inequalities in mortality can be compared between coun-
tries even if their educational distributions are different.
The RII is a relative measure which can be interpreted as
the rate ratio of mortality among those with the very lowest
educational level compared to those with the very highest
educational level. SIIs were calculated from the RIIs and
the age-standardized mortality rates (ASMR) in the general
population using the formula: SII = 2 9 ASMR 9 (-
RII - 1)/(RII ? 1). The SII is an absolute measure which
can be interpreted as the rate difference of mortality
between those with the very lowest and those with the very
highest educational level (Mackenbach and Kunst 1997;
Mackenbach et al. 2008). We calculated 95% CIs using
Table 1 Key characteristics of the datasets used in the analysis
Country Type Years Census date Geographic
coverage
Ages
included
Number
of deaths
Number of
person years
Finland Longitudinal Dec 31, 1970 to Dec 31, 1980 31.12.1970 National 35–79 310,253 19,401,076
Dec 31, 1980 to Dec 31, 1990 31.12.1980 297,925 22,132,564
Dec 31, 1990 to Dec 31, 2000 31.12.1990 271,246 25,148,551
Dec 31, 2000 to Dec 31, 2010 31.12.2000 247,441 27,446,463
Norway Longitudinal Nov, 1970 to Dec, 1980 Nov, 1970 National 40–79 237,790 14,097,453
Nov, 1980 to Dec, 1990 Nov, 1980 235,645 13,804,407
Nov, 1990 to Dec, 2001 Nov, 1990 227,996 16,372,733
Nov, 2001 to Dec, 2009 Nov, 2001 133,675 14,050,440
England/Wales Longitudinal Apr 25, 1971 to Apr 4,1981 25.4. 1971 National 35–79 40,543 2,315,725
Apr 5, 1981 to Apr 20,1991 5.4.1981 36,715 2,453,660
Apr 21, 1991 to Apr 28, 2001 21.4.1991 31,472 2,579,226
Apr 29, 2001 to Dec 31, 2009 29.4. 2001 22,009 2,431,057
France Longitudinal 10.1.1975 to 9.1.1982 10.1.1975 National 35–79 16,940 1,397,501
10.1.1982 to 9.1.1990 10.1.1982 19,236 1,689,497
10.1.1990 to 9.1.1999 10.1.1990 19,331 2,113,937
10.1.1999 to 31.12.2007 10.1.1999 15,663 1,745,199
Italy (Turin) Longitudinal Oct 24, 1971 to Oct 24, 1981 24.10.1971 City 35–79 58,426 4,689,534
Oct 25, 1981 to Oct 19, 1991 25.10.1981 56,947 5,118,272
Oct 20, 1991 to Oct 20, 2001 20.10.1991 46,181 4,736,315
Oct 21, 2001 to Dec 31, 2010 21.10.2001 35,056 4,283,032
Hungary CS, unlinked 1971–1974 1.1.1973 National 35–79 340,511 19,754,780
1978–1981 1.1.1980 393,590 20,180,700
1988–1991 1990 385,974 20,576,688
1999–2002 2001 369,773 21,031,348
Data from England and Wales and France concern a 1% representative sample of the total population
In France, only those born in mainland France were included (excluding overseas territories and abroad)
CS cross-sectional
Long-term trends of inequalities in mortality in 6 European countries 129
123
bootstrapping of 1000 replicas. As our analysis of mortality
by occupational class only involved two classes (manual
and non-manual), we used simple Rate Ratios and Rate
Differences with the non-manual class as the reference
group for this socioeconomic indicator.
Results
In this study, a total of 3,850,338 deaths were observed.
Tables 2 and 3 present all-cause mortality rates by edu-
cation for each country and time-period, and changes in
mortality rates over time. Full details on mortality rates
by cause of death, sex, time-period and education in each
country are given in web appendix Table 3. In the early
1970s, all-cause mortality was already highest among the
lowest educated in all countries among both men and
women, with the exception of Hungarian women for
whom mortality was slightly higher among the high
educated, and even 1.4 times higher among the higher
educated in the early 1980s. Since the early 1980s,
mortality gradually decreased over time in all educational
groups, except in Hungary where mortality among the
low and middle educated increased until the early 1980s
(women) and early 1990s (men) and then also started to
decline.
Relative (i.e., percentage) declines were almost always
largest among the high educated. As a result, relative
inequalities in all-cause mortality, as measured by the RII,
went up in most countries (Fig. 1). The increase was
strongest in Hungary, where the RII went up from a level
that was among the lowest among men, and even slightly
below 1.00 among women, in the early 1970s to a level that
was higher than that in any other country in the early
2000s. Norway also stands out as a country with a rela-
tively steep increase of the RII, both among men and
women. In France (men and women) and Italy (women), on
the other hand, RIIs did not change significantly over time,
as indicated by the overlapping 95% confidence intervals
of the first and last observation periods.’’
Trends in absolute inequalities in all-cause mortality by
education were more variable (Fig. 2). This is due to the
fact that absolute declines in mortality were largest among
the low educated in some countries [England and Wales,
France and Italy (Turin)], but not in others (Norway and
Hungary) (Tables 2, 3). Over time, absolute inequalities in
mortality as measured by the SII went down among men in
most countries, but not in Norway where the SII increased
until the late 1990s and only then started to decline
(Fig. 2a). In Hungary, due to the enormous rise of mortality
among low educated men, the SII for all-cause mortality
increased from a relatively low level in the early 1970s to a
very high level in the early 2000s.
Among women, although differences in absolute mor-
tality declines between the low and high educated were
usually smaller than among men (Tables 2, 3), absolute
inequalities in all-cause mortality among women increased
in Hungary and Norway, and decreased in Finland, Eng-
land and Wales and Italy (Turin), as they did in men
(Fig. 2b).
Figure 2 also shows that the role of cardiovascular dis-
eases in generating inequalities in all-cause mortality has
changed considerably over time, particularly in Finland,
England and Wales, and Norway. In these countries, car-
diovascular diseases used to be the main contributor to
inequalities in all-cause mortality among both men and
women, but in the most recent periods this was no longer
the case. For example, among Finnish women the contri-
bution of cardiovascular diseases to inequality in all-cause
mortality (calculated as 100 9 (SII for cardiovascular
diseases mortality)/(SII for all-cause mortality)) decreased
from 72% in the early 1970s to 36% in the late 2000s. In
most countries, absolute decreases in cardiovascular mor-
tality rates were largest among the lower educated (web
appendix Table 3), and as a result, absolute inequalities in
cardiovascular mortality declined considerably (Fig. 2),
although relative inequalities went up (web appendix
figure 1).
The declining contribution of cardiovascular diseases to
inequality in all-cause mortality implies an increasing
contribution of other causes of death: in three out of six
countries the contribution of cancer mortality to inequality
in all-cause mortality increased among both men and
women. The continued increase of absolute inequalities in
Norway until the late 1990s was due to a rise of inequalities
in cancer and other diseases among both men and women,
which more than compensated for the decline of inequali-
ties in cardiovascular disease (Fig. 2). Furthermore, the
massive rise of inequalities in all-cause mortality in Hun-
gary was due to rises seen for several causes of death, and
to a reversal of formerly ‘negative’ inequalities in cancer
mortality (indicating higher mortality among the high
educated) to ‘positive’ inequalities in the last observation
period.
All-cause mortality trends by occupational class were
partly similar to trends by education. In the four countries
for which data were available (Finland, England and
Wales, France and Italy—men only), mortality rates
declined among both manual and non-manual workers,
with relative declines usually being largest among non-
manual workers, and differences in absolute declines being
more variable (web appendix Table 4). This resulted in
increasing relative inequalities by occupational class in
most countries, as we observed for inequalities by educa-
tion; however, absolute inequalities decreased in France
only, and were stable in the other three countries (Fig. 3).
130 R. de Gelder et al.
123
Ta
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98
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1.5
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19
80
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98
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81
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(18
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18
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92
1.6
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94
9.2
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57
5.4
(15
37
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16
11
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12
90
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26
0.4
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32
4.3
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19
85
–1
98
91
67
3.1
(16
60
.9–
16
83
.3)
18
20
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80
6.8
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83
4.2
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48
5.1
(14
53
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15
15
.6)
11
56
.7(1
13
1.8
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18
3.7
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19
90
–1
99
41
48
5.2
(14
75
.3–
14
95
.8)
16
55
.3(1
64
2.5
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66
8.8
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34
4.4
(13
18
.4–
13
71
.7)
99
7.7
(97
5.5
–1
02
0.9
)
19
95
–1
99
91
30
4.9
(12
96
.2–
13
14
.6)
14
99
.0(1
48
4.3
–1
51
1.6
)1
21
3.6
(11
92
.4–
12
36
.7)
84
2.8
(82
4.2
–8
61
.0)
20
00
–2
00
41
13
0.6
(11
23
.1–
11
39
.2)
13
63
.2(1
34
9.7
–1
37
5.9
)1
06
3.8
(10
47
.1–
10
80
.6)
71
8.3
(70
3.8
–7
33
.1)
20
05
–2
00
91
01
6.6
(10
09
.8–
10
24
.2)
12
94
.8(1
28
0.2
–1
30
9.4
)1
00
3.5
(99
0.4
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01
8.2
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27
.6(6
15
.8–
63
9.0
)
Per
cen
tch
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st—
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t(9
5%
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-5
3.9
%(-
54
.3to
-5
3.5
%)
-4
3.9
%(-
44
.6to
-4
3.2
%)
-4
3.3
%(-
44
.9to
-4
1.6
%)
-6
2.4
%(-
63
.6to
-6
1.1
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-1
18
8.7
(-1
20
4.6
to-
11
72
.8)
-1
01
2.9
(-1
03
4.7
to-
99
1.1
)-
76
6.0
(-8
15
.6to
-7
16
.4)
-1
04
1.6
(-1
09
0.3
to-
99
2.9
)
No
rway
19
70
–1
97
41
72
7.1
(17
16
.0–
17
38
.0)
18
27
.9(1
81
0.0
–1
84
5.5
)1
59
5.6
(15
78
.1–
16
15
.6)
13
29
.7(1
28
7.5
–1
37
2.2
)
19
75
–1
97
91
71
1.9
(17
00
.8–
17
23
.6)
18
38
.6(1
81
8.5
–1
86
0.4
)1
59
9.6
(15
74
.9–
16
16
.0)
12
55
.0(1
21
6.2
–1
29
3.0
)
19
80
–1
98
41
62
8.1
(16
17
.6–
16
38
.8)
17
80
.5(1
76
4.6
–1
80
1.0
)1
51
4.2
(14
94
.6–
15
30
.2)
11
80
.7(1
14
9.7
–1
21
1.5
)
19
85
–1
98
91
59
4.0
(15
81
.9–
16
03
.9)
17
92
.1(1
76
8.9
–1
81
2.8
)1
47
0.3
(14
49
.8–
14
87
.7)
10
85
.0(1
05
3.5
–1
11
5.3
)
19
90
–1
99
41
41
2.3
(14
02
.0–
14
22
.0)
16
56
.4(1
63
8.5
–1
67
5.8
)1
31
0.6
(12
93
.4–
13
23
.4)
94
5.7
(91
7.0
–9
67
.1)
19
95
–1
99
91
24
5.4
(12
36
.6–
12
54
.7)
15
72
.3(1
55
4.2
–1
59
6.1
)1
14
6.9
(11
33
.5–
11
62
.7)
80
0.8
(78
2.5
–8
18
.8)
20
00
–2
00
41
02
8.2
(10
20
.2–
10
35
.3)
13
81
.2(1
36
1.1
–1
40
5.7
)9
53
.4(9
40
.5–
96
5.9
)6
60
.3(6
42
.4–
67
7.8
)
20
05
–2
00
99
33
.8(9
22
.9–
94
3.8
)1
28
4.2
(12
53
.5–
13
10
.3)
89
2.4
(88
0.0
–9
06
.2)
59
6.5
(57
8.5
–6
14
.0)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-4
5.9
%(-
46
.7to
-4
5.2
%)
-2
9.7
%(-
31
.3to
-2
8.2
%)
-1
1.1
%(-
45
.4to
-4
2.8
%)
-5
5.1
%(-
57
.3to
52
.9%
)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-7
93
.3(-
81
0.1
to-
77
6.5
)-
54
3.7
(-5
74
.1to
-5
13
.3)
-7
03
.2(-
73
2.0
to-
67
4.4
)-
73
3.2
(-7
81
.9to
-6
84
.5)
En
gla
nd
and
Wal
es
19
70
–1
97
41
96
9.3
(19
35
.0–
20
02
.1)
19
65
.1(1
92
8.3
–2
00
5.4
)N
AN
A1
49
3.0
(13
33
.2–
16
55
.0)
19
75
–1
97
91
86
8.1
(18
36
.0–
19
02
.0)
18
98
.5(1
86
0.6
–1
93
5.9
)N
AN
A1
22
0.7
(11
06
.9–
13
32
.8)
19
80
–1
98
41
65
0.6
(16
22
.5–
16
82
.0)
17
03
.0(1
66
5.3
–1
73
5.6
)N
AN
A1
06
3.8
(97
1.5
–1
15
4.9
)
19
85
–1
98
91
48
6.7
(14
56
.2–
15
16
.7)
15
44
.9(1
51
5.3
–1
57
6.5
)N
AN
A9
76
.2(8
95
.2–
10
54
.0)
19
90
–1
99
41
33
4.1
(13
05
.5–
13
60
.3)
13
89
.4(1
35
7.3
–1
42
1.4
)N
AN
A8
95
.6(8
33
.2–
95
9.7
)
19
95
–1
99
91
15
0.0
(11
25
.6–
11
72
.6)
12
15
.1(1
18
4.9
–1
24
5.8
)N
AN
A7
55
.0(7
08
.6–
80
6.1
)
20
00
–2
00
49
94
.1(9
68
.9–
10
14
.2)
10
70
.4(1
04
3.1
–1
09
7.9
)N
AN
A6
77
.0(6
29
.4–
72
2.6
)
20
05
–2
00
98
09
.9(7
85
.5–
83
3.1
)8
76
.7(8
46
.6–
90
6.7
)N
AN
A5
59
.2(5
14
.6–
60
6.8
)
Long-term trends of inequalities in mortality in 6 European countries 131
123
Ta
ble
2co
nti
nu
ed
Co
un
try
/per
iod
Ed
uca
tio
nal
lev
el
To
tal
Lo
wM
idd
leH
igh
AS
MR
95
%C
IA
SM
R9
5%
CI
AS
MR
95
%C
IA
SM
R9
5%
CI
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-5
8.9
%(-
60
.3to
-5
7.5
%)
-5
5.4
%(-
57
.1to
-5
3.8
%)
NA
NA
-6
2.5
%(-
67
.3to
-5
7.2
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-1
15
9.4
(-1
20
0.5
to-
11
18
.3)
-1
08
8.4
(-1
13
7.3
to-
10
39
.5)
NA
NA
-9
33
.8(-
11
01
.2to
-7
66
.4)
Fra
nce
19
75
–1
97
91
46
1.9
(14
28
.5–
14
95
.3)
15
65
.1(1
52
5.7
–1
60
1.8
)1
15
5.1
(10
61
.9–
12
61
.0)
86
1.9
(74
9.5
–9
82
.4)
19
80
–1
98
41
46
6.4
(14
24
.1–
15
08
.8)
15
88
.4(1
53
6.3
–1
64
2.8
)1
17
1.7
(10
67
.8–
12
79
.0)
90
3.9
(76
9.4
–1
04
6.6
)
19
85
–1
98
91
42
3.7
(13
86
.9–
14
60
.4)
15
54
.5(1
50
8.2
–1
60
2.6
)1
18
1.9
(10
95
.9–
12
69
.2)
88
0.0
(77
2.1
–9
89
.1)
19
90
–1
99
41
19
9.5
(11
71
.1–
12
27
.9)
13
51
.7(1
31
5.6
–1
39
1.5
)1
03
0.8
(97
2.2
–1
08
4.8
)6
74
.4(5
99
.1–
74
9.5
)
19
95
–1
99
91
16
0.8
(11
30
.4–
11
91
.1)
13
56
.5(1
31
2.8
–1
40
4.1
)9
86
.7(9
34
.8–
14
04
.1)
63
0.0
(55
6.7
–7
06
.8)
20
00
–2
00
49
96
.6(9
71
.9–
10
21
.4)
11
96
.6(1
15
5.8
–1
23
5.8
)8
99
.8(8
54
.3–
94
0.2
)5
48
.5(4
91
.8–
60
9.8
)
20
05
–2
00
99
33
.4(9
06
.9–
95
9.9
)1
15
0.3
(11
01
.8–
11
96
.7)
86
5.3
(82
2.1
–9
07
.5)
57
6.5
(52
0.8
–6
33
.2)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-3
6.2
%(-
38
.5to
-3
3.8
%)
-2
6.5
%(-
30
.1to-
23
.1%
)-
25
.1%
(-3
1.7
to-
18
.0%
)-
33
.1%
(-4
3.2
to-
19
.9%
)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-5
28
.5(-
57
1.1
to-
48
5.9
)-
41
4.8
(-4
75
.6to
-3
54
.0)
-2
89
.8(-
39
8.1
to-
18
1.5
)-
28
5.4
(-4
14
.7to
-1
56
.1)
Ital
y(T
uri
n)
19
70
–1
97
41
69
4.5
(16
69
.0–
17
18
.6)
17
29
.6(1
70
3.2
–1
75
8.0
)1
59
1.0
(15
01
.0-
16
78
.2)
13
21
.8(1
22
9.2
–1
41
4.7
)
19
75
–1
97
91
60
7.5
(15
84
.1–
16
32
.2)
16
49
.9(1
62
4.6
–1
67
6.9
)1
43
7.0
(13
51
.8–
15
13
.5)
12
89
.8(1
20
0.9
–1
38
5.5
)
19
80
–1
98
41
42
9.4
(14
07
.2–
14
51
.0)
14
82
.2(1
45
8.1
–1
50
5.6
)1
25
9.1
(11
96
.3–
13
28
.9)
10
34
.9(9
62
.5–
10
98
.7)
19
85
–1
98
91
25
1.0
(12
32
.2–
12
70
.6)
13
09
.9(1
28
6.9
–1
33
2.7
)1
09
6.0
(10
39
.3–
11
48
.5)
91
0.9
(84
8.5
–9
75
.7)
19
90
–1
99
41
12
0.3
(11
02
.2–
11
38
.3)
11
92
.7(1
17
1.7
–1
21
4.4
)9
78
.6(9
33
.2–
10
25
.6)
80
8.9
(75
8.4
–8
60
.3)
19
95
–1
99
99
41
.2(9
24
.8–
95
7.3
)1
01
9.9
(99
9.0
–1
04
1.9
)7
92
.0(7
54
.0–
83
0.5
)6
69
.1(6
23
.3–
71
2.9
)
20
00
–2
00
48
07
.5(7
93
.4–
82
1.6
)9
04
.5(8
84
.3–
92
4.6
)6
68
.6(6
38
.5–
69
8.7
)5
71
.8(5
33
.8–
60
9.1
)
20
05
–2
00
97
15
.2(6
99
.7–
73
1.6
)8
26
.1(8
03
.8–
85
0.0
)5
96
.4(5
65
.0–
62
5.6
)4
85
.7(4
48
.9–
52
4.4
)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-5
7.8
%(-
59
.0to
-5
6.6
%)
-5
2.2
%(-
53
.7to
-5
0.7
%)
-6
2.5
%(-
65
.1to
-5
9.6
%)
-6
3.3
%(-
66
.9to
-5
9.1
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-9
79
.3(-
10
08
.8to
-9
49
.8)
-9
03
.5(-
93
9.3
to-
86
7.7
)-
99
4.6
(-1
08
8.2
to-
90
1.0
)-
83
6.1
(-9
36
.2to
-7
36
.0)
Hu
ng
ary
19
70
–1
97
41
99
7.4
(19
89
.3–
20
04
.7)
20
33
.4(2
02
3.9
–2
04
4.4
)1
74
8.1
(17
14
.1–
17
82
.5)
17
69
.3(1
73
0.3
–1
81
0.9
)
19
80
–1
98
42
29
6.8
(22
87
.8–
23
05
.2)
23
73
.6(2
36
3.9
–2
38
6.3
)1
97
0.4
(19
44
.4–
20
05
.3)
19
98
.4(1
96
6.1
–2
02
9.9
)
19
90
–1
99
42
43
6.0
(24
26
.5–
24
45
.2)
26
50
.9(2
64
0.2
–2
66
2.6
)2
10
3.9
(20
74
.4–
21
34
.5)
14
09
.4(1
38
5.2
–1
43
3.8
)
20
00
–2
00
42
19
4.5
(21
83
.9–
22
02
.3)
26
23
.7(2
61
4.2
–2
63
8.5
)1
46
9.5
(14
49
.4–
14
84
.2)
10
20
.4(1
00
3.3
–1
03
3.3
)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
?9
.9%
(9.2
–1
0.5
%)
?2
9.0
%(2
8.2
–2
9.9
%)
-1
5.9
%(-
17
.8to
-1
3.9
%)
-4
2.3
%(-
43
.9to
-4
0.8
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
?1
97
.1(1
85
.1–
20
9.1
)?
59
0.4
(57
4.5
–6
06
.2)
-2
78
.6(-
31
6.9
to-
24
0.2
)-
74
9.0
(-7
92
.0to
-7
06
.0)
InE
ng
lan
dan
dW
ales
,m
ort
alit
yra
tes
amo
ng
‘lo
w’
and
‘mid
dle
’ed
uca
ted
wer
eco
mb
ined
bec
ause
‘mid
dle
’ed
uca
tio
nis
no
tav
aila
ble
inth
e1
98
0s
and
19
90
s
ASMR
age-
stan
dar
diz
edm
ort
alit
yra
te,NA
no
tav
aila
ble
,CI
con
fid
ence
inte
rval
132 R. de Gelder et al.
123
Ta
ble
3A
ge-
stan
dar
diz
edal
l-ca
use
mo
rtal
ity
rate
sp
er1
00
.00
0p
erso
ny
ears
,b
yed
uca
tio
nal
gro
up
,w
om
en
Co
un
try
/per
iod
Ed
uca
tio
nal
lev
el
To
tal
Lo
wM
idd
leH
igh
AS
MR
95
%C
IA
SM
R9
5%
CI
AS
MR
95
%C
IA
SM
R9
5%
CI
Fin
lan
d
19
70
–1
97
41
04
1.7
(10
33
.8–
10
49
.3)
10
76
.9(1
06
8.4
–1
08
5.2
)8
09
.7(7
81
.6–
83
6.7
)7
94
.5(7
63
.0–
82
4.6
)
19
75
–1
97
98
81
.8(8
74
.6–
88
8.7
)9
16
.2(9
07
.4–
92
3.9
)7
14
.1(6
91
.8–
73
6.0
)6
73
.3(6
47
.3–
70
0.0
)
19
80
–1
98
47
94
.8(7
88
.4–
80
1.2
)8
30
.1(8
22
.7–
83
7.2
)6
74
.4(6
55
.3–
69
1.8
)5
91
.9(5
70
.7–
61
3.4
)
19
85
–1
98
97
58
.2(7
51
.8–
76
4.3
)8
00
.0(7
92
.0–
80
7.6
)6
66
.8(6
51
.3–
68
2.6
)5
68
.5(5
49
.6–
58
7.7
)
19
90
–1
99
46
79
.2(6
73
.3–
68
4.7
)7
39
.8(7
32
.5–
74
7.1
)5
84
.2(5
71
.1–
59
7.1
)5
08
.4(4
92
.1–
52
5.1
)
19
95
–1
99
95
95
.3(5
90
.0–
60
0.2
)6
69
.8(6
61
.6–
67
7.9
)5
24
.4(5
13
.7–
53
5.7
)4
34
.3(4
21
.0–
44
7.7
)
20
00
–2
00
45
27
.9(5
23
.1–
53
2.7
)6
41
.3(6
32
.4–
64
9.7
)4
70
.0(4
61
.2–
47
9.8
)3
74
.3(3
63
.2–
38
4.4
)
20
05
–2
00
94
69
.7(4
64
.6–
47
4.1
)6
13
.1(6
02
.0–
62
3.4
)4
37
.3(4
29
.3–
44
5.4
)3
36
.1(3
27
.2–
34
4.4
)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-5
4.9
%(-
55
.4to-
54
.3%
)-
43
.1%
(-4
4.2
to-
42
.0%
)-
46
.0%
(-4
8.2
to-
43
.9%
)-
57
.7%
(-5
9.6
to-
55
.7%
)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-5
72
.0(-
58
1.1
to-
56
2.9
)-
46
3.8
(-4
77
.4to
-4
50
.2)
-3
72
.4(-
40
1.1
to-
34
3.7
)-
45
8.4
(-4
90
.4to
-4
26
.4)
No
rway
19
70
–1
97
49
38
.3(9
29
.5–
94
5.8
)1
00
0.7
(99
2.4
–1
01
1.2
)7
74
.5(7
58
.1–
78
6.5
)6
89
.2(6
58
.5–
72
9.9
)
19
75
–1
97
98
74
.5(8
65
.1–
88
4.4
)9
35
.8(9
27
.6–
94
7.8
)7
53
.4(7
37
.6–
76
8.3
)6
53
.6(6
23
.0–
68
1.1
)
19
80
–1
98
47
99
.3(7
92
.0–
80
6.4
)8
57
.5(8
48
.7–
86
6.3
)7
00
.2(6
87
.5–
71
1.0
)5
97
.1(5
74
.7–
62
3.2
)
19
85
–1
98
98
02
.3(7
95
.2–
81
0.1
)8
72
.8(8
62
.6–
88
5.6
)7
00
.5(6
89
.2–
71
2.5
)5
86
.4(5
61
.1–
62
0.4
)
19
90
–1
99
47
37
.5(7
30
.9–
74
4.1
)8
35
.6(8
26
.2–
84
6.7
)6
45
.8(6
35
.1–
65
6.3
)5
07
.5(4
86
.4–
52
2.8
)
19
95
–1
99
96
93
.0(6
86
.7–
70
0.1
)8
49
.3(8
37
.5–
86
1.5
)6
02
.5(5
91
.6–
61
1.4
)4
47
.5(4
32
.2–
46
2.7
)
20
00
–2
00
46
12
.8(6
06
.1–
61
9.2
)7
89
.8(7
78
.1–
80
0.6
)5
37
.9(5
28
.8–
54
7.3
)4
03
.6(3
91
.5–
41
8.4
)
20
05
–2
00
95
72
.1(5
64
.4–
57
9.7
)7
64
.3(7
49
.0–
78
1.6
)5
13
.5(5
00
.4–
52
3.2
)3
72
.3(3
60
.9–
38
8.6
)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-3
9.0
%(-
40
.0to
-3
8.1
%)
-2
3.6
%(-
25
.3to
-2
1.6
%)
-3
3.7
%(-
35
.7to
-3
1.6
%)
–4
6.0
%(-
49
.6to
-4
2.1
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-3
66
.2(-
37
7.9
to-
35
4.5
)-
23
6.4
(-2
57
.4to
-2
15
.4)
-2
61
.0(-
28
0.8
to-
24
1.2
)–
31
6.9
(-3
57
.4to
-2
76
.4)
En
gla
nd
and
Wal
es
19
70
–1
97
41
11
4.9
(10
91
.7–
11
38
.8)
10
90
.8(1
06
6.0
–1
11
4.2
)N
AN
A8
28
.5(7
24
.5–
94
8.8
)
19
75
–1
97
91
00
1.1
(97
9.8
–1
02
1.8
)1
00
9.4
(98
6.0
–1
03
4.4
)N
AN
A6
86
.2(6
02
.0–
77
6.2
)
19
80
–1
98
49
29
.9(9
09
.3–
95
1.4
)9
42
.6(9
22
.4–
96
4.3
)N
AN
A6
77
.8(5
96
.4–
76
0.2
)
19
85
–1
98
98
42
.7(8
22
.5–
86
3.5
)8
57
.0(8
37
.3–
87
7.6
)N
AN
A6
30
.9(5
59
.9–
70
1.5
)
19
90
–1
99
47
95
.6(7
76
.3–
81
5.3
)8
10
.9(7
89
.2–
83
2.5
)N
AN
A5
57
.2(5
05
.0–
61
7.4
)
19
95
–1
99
97
21
.2(7
02
.9–
74
0.9
)7
47
.0(7
25
.6–
76
9.3
)N
AN
A4
59
.2(4
15
.7–
50
5.3
)
20
00
–2
00
46
39
.7(6
23
.5–
65
6.7
)6
73
.9(6
55
.0–
69
3.7
)N
AN
A4
43
.4(4
03
.0–
48
4.4
)
20
05
–2
00
95
69
.7(5
51
.0–
58
7.8
)6
10
.8(5
90
.2–
63
4.4
)N
AN
A4
17
.5(3
78
.2–
45
9.5
)
Long-term trends of inequalities in mortality in 6 European countries 133
123
Ta
ble
3co
nti
nu
ed
Co
un
try
/per
iod
Ed
uca
tio
nal
lev
el
To
tal
Lo
wM
idd
leH
igh
AS
MR
95
%C
IA
SM
R9
5%
CI
AS
MR
95
%C
IA
SM
R9
5%
CI
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-4
8.9
%(-
50
.8to
-4
6.7
%)
-4
4.0
%(-
46
.4to
-4
1.6
%)
NA
NA
-4
9.6
%(-
57
.0to
-3
9.7
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-5
45
.2(–
57
5.1
to-
51
5.3
)–
48
0.0
(–5
12
.7to
-4
47
.3)
NA
NA
-4
11
.0(-
53
0.3
to-
29
1.7
)
Fra
nce
19
75
–1
97
96
65
.0(6
45
.2–
68
4.8
)6
81
.0(6
60
.0–
70
3.3
)5
61
.3(4
89
.4–
64
1.5
)4
85
.8(3
72
.7–
59
1.8
)
19
80
–1
98
46
19
.8(5
95
.4–
64
4.2
)6
37
.4(6
11
.4–
66
4.4
)5
23
.3(4
36
.5–
61
3.6
)4
80
.9(3
46
.5–
62
5.6
)
19
85
–1
98
95
89
.9(5
68
.9–
61
0.9
)6
14
.0(5
90
.9–
63
7.0
)4
78
.3(4
11
.4–
54
6.9
)3
66
.1(2
77
.8–
45
8.7
)
19
90
–1
99
45
10
.2(4
93
.5–
52
6.8
)5
44
.9(5
24
.0–
56
6.0
)4
12
.0(3
72
.6–
45
4.4
)3
37
.3(2
70
.4–
40
8.8
)
19
95
–1
99
94
89
.9(4
71
.9–
50
7.8
)5
48
.2(5
24
.3–
57
3.2
)3
64
.6(3
28
.1–
40
1.9
)3
30
.3(2
65
.0–
40
2.0
)
20
00
–2
00
44
24
.1(4
09
.2–
43
8.9
)4
75
.4(4
54
.2–
49
5.7
)3
79
.1(3
48
.6–
40
6.5
)2
87
.2(2
41
.5–
33
1.2
)
20
05
–2
00
94
27
.9(4
11
.1–
44
4.6
)4
96
.7(4
72
.3–
52
6.1
)3
81
.5(3
51
.5–
41
2.8
)2
82
.6(2
38
.3–
32
7.2
)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-3
5.7
%(-
38
.8to
-3
2.4
%)
-2
7.1
%(-
31
.5to
-2
2.7
%)
-3
2.0
%(-
41
.8to
-2
0.4
%)
-4
1.8
%(–
55
.5to
-2
0.4
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-2
37
.1(-
26
3.0
to-
21
1.2
)-
18
4.3
(-2
18
.8to
-1
49
.8)
-1
79
.8(-
26
1.8
to-
97
.8)
-2
03
.2(-
32
1.4
to-
85
.0)
Ital
y(T
uri
n)
19
70
–1
97
48
66
.2(8
51
.2–
88
1.5
)8
77
.6(8
62
.0–
89
3.5
)7
15
.7(6
54
.7–
77
6.4
)6
15
.1(4
99
.0–
74
1.1
)
19
75
–1
97
98
09
.4(7
94
.2–
82
4.3
)8
19
.7(8
04
.5–
83
5.0
)6
87
.1(6
34
.4–
74
9.2
)5
93
.2(4
99
.2–
70
6.1
)
19
80
–1
98
47
18
.7(7
06
.8–
73
1.4
)7
24
.5(7
11
.6–
73
8.3
)6
20
.2(5
75
.4–
66
5.6
)5
97
.6(5
08
.8–
68
9.3
)
19
85
–1
98
96
20
.9(6
09
.5–
63
3.5
)6
29
.4(6
16
.0–
64
2.8
)5
43
.1(5
01
.9–
58
3.1
)5
06
.0(4
28
.7–
58
2.1
)
19
90
–1
99
45
56
.7(5
45
.7–
56
8.1
)5
74
.0(5
59
.4–
58
7.1
)4
63
.2(4
31
.8–
49
5.4
)4
32
.6(3
78
.0–
48
8.7
)
19
95
–1
99
94
95
.3(4
84
.2–
50
6.6
)5
07
.9(4
95
.0–
52
0.2
)4
47
.1(4
17
.4–
47
7.5
)4
15
.4(3
72
.5–
46
2.0
)
20
00
–2
00
44
22
.9(4
12
.6–
43
3.2
)4
38
.1(4
25
.8–
45
0.7
)4
06
.9(3
82
.1–
43
1.0
)3
80
.6(3
47
.0–
41
6.4
)
20
05
–2
00
93
67
.8(3
57
.1–
37
7.6
)4
01
.7(3
86
.7–
41
6.4
)3
16
.5(2
95
.8–
33
9.8
)2
93
.2(2
60
.3–
32
7.0
)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-5
7.5
%(-
58
.8to
-5
6.2
%)
-5
4.2
%(-
56
.0to
-5
2.4
%)
-5
5.8
%(-
60
.7to
-5
0.6
%)
–5
2.3
%(-
60
.8to
-3
9.7
%)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-4
98
.4(-
51
6.7
to-
48
0.1
)-
47
5.9
(-4
97
.5to
-4
54
.3)
-3
99
.2(-
46
3.9
to-
33
4.5
)–
32
1.9
(-4
47
.5to
-1
96
.3)
Hu
ng
ary
19
70
–1
97
41
23
0.6
(12
24
.2–
12
36
.0)
12
28
.5(1
22
2.5
–1
23
4.8
)1
24
4.7
(12
14
.8–
12
80
.7)
13
20
.7(1
25
2.0
–1
38
7.9
)
19
80
–1
98
41
27
6.5
(12
70
.2–
12
82
.4)
12
74
.5(1
26
8.8
–1
28
1.0
)1
28
6.9
(12
61
.6–
13
11
.4)
17
31
.8(1
67
1.0
–1
79
7.6
)
19
90
–1
99
41
21
3.6
(12
07
.8–
12
19
.3)
12
48
.9(1
24
2.9
–1
25
7.8
)1
23
3.5
(12
12
.2–
12
57
.1)
82
6.8
(79
5.2
–8
52
.6)
20
00
–2
00
41
02
2.7
(10
18
.1–
10
27
.1)
11
45
.8(1
13
8.9
–1
15
3.0
)7
18
.3(7
08
.4–
73
0.6
)7
06
.1(6
83
.0–
72
2.3
)
Per
cen
tch
ang
ela
st—
firs
t(9
5%
CI)
-1
6.9
%(-
27
.5to
-1
6.4
%)
-6
.7%
(-7
.59
6to
-6
.0%
)-
42
.3%
(-4
4.0
to-
40
.5%
)-
46
.5%
(-4
9.5
to-
43
.4%
)
Ab
solu
tech
ang
ela
st—
firs
t(9
5%
CI)
-2
07
.9(-
21
5.3
to-
20
0.4
)-
82
.8(-
92
.1to
-7
3.4
)-
52
6.4
(-5
61
.1to
-4
91
.6)
-6
14
.6(-
68
5.4
to-
54
3.8
)
InE
ng
lan
dan
dW
ales
,m
ort
alit
yra
tes
amo
ng
‘lo
w’
and
‘mid
dle
’ed
uca
ted
wer
eco
mb
ined
bec
ause
‘mid
dle
’ed
uca
tio
nis
no
tav
aila
ble
inth
e1
98
0s
and
19
90
s
ASMR
age-
stan
dar
diz
edm
ort
alit
yra
te,NA
no
tav
aila
ble
,CI
con
fid
ence
inte
rval
134 R. de Gelder et al.
123
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Rela
�ve
Inde
x of
Ineq
ualit
y (R
II)
Period
3 5
4.0
4.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Rela
�ve
Inde
x of
Ineq
ualit
y (R
II)
Period
Finland Norway England & Wales France Italy (Turin) Hungary
a
b
Fig. 1 Trends in relative index
of inequality for all-cause
mortality by education for:
a men, and b women. In
England and Wales, RIIs for the
period 1980–1999 could not be
calculated because ‘middle’
education was not available
Long-term trends of inequalities in mortality in 6 European countries 135
123
-500
0
500
1000
1500
2000
2500
3000
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
England & Wales
-500
0
500
1000
1500
2000
2500
3000
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Finland
-500
0
500
1000
1500
2000
2500
3000
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Norway
2500
3000
y (S
II) France 2500
3000
y (S
II) Italy (Turin) 2500
3000
y (S
II) Hungary
-500
0
500
1000
1500
2000
2500
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
France
-500
0
500
1000
1500
2000
2500
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Italy (Turin)
-500
0
500
1000
1500
2000
2500
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Hungary
all causes cardiovascular diseases cancer other diseases external causes
-250
0
250
500
750
1000
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
England & Wales
-250
0
250
500
750
1000
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Finland
-250
0
250
500
750
1000
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Norway
750
1000
ty (S
II) France750
1000
y (S
II) Italy (Turin)750
1000
y (S
II) Hungary
-250
0
250
500
750
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
France
-250
0
250
500
750
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Italy (Turin)
-250
0
250
500
750
1970
-197
4
1975
-197
9
1980
-198
4
1985
-198
9
1990
-199
4
1995
-199
9
2000
-200
4
2005
-200
9
Slop
e In
dex
of In
equa
lity
(SII)
Period
Hungary
all causes cardiovascular diseases cancer other diseases external causes
a
b
Fig. 2 Trends in slope index of inequality for all-cause and cause-specific mortality by education, by country: a men, b women. In England and
Wales, SIIs for the period 1980–1999 could not be calculated because ‘middle’ education was not available
136 R. de Gelder et al.
123
0
0.5
1
1.5
2
2.5
3
3.5
4
Mor
talit
y ra
te ra
�o b
etw
een
man
ual a
nd
non-
man
ual w
orke
rs
Period
Finland
0
0.5
1
1.5
2
2.5
3
3.5
4
Mor
talit
y ra
te ra
�o b
etw
een
man
ual a
nd
non-
man
ual w
orke
rs
Period
England & Wales
3.5
4
and France
3.5
4
al a
nd Turin (Italy)
0
0.5
1
1.5
2
2.5
3
3.5
4
Mor
talit
y ra
te ra
�o b
etw
een
man
ual a
nd
non-
man
ual w
orke
rs
Period
France
0
0.5
1
1.5
2
2.5
3
3.5
4
Mor
talit
y ra
te ra
�o b
etw
een
man
ual a
nd
non-
man
ual w
orke
rs
Period
Turin (Italy)
All causes Cardiovascular diseases Cancer Other diseases External causes
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Fig. 3 a Trends in rate ratio for all-cause and cause-specific mortality by occupational class, by country, men. b Trends in rate difference for all-
cause and cause-specific mortality by occupational class, by country, men
Long-term trends of inequalities in mortality in 6 European countries 137
123
Discussion
Summary
In all countries except Hungary, all-cause mortality
declined strongly over time in all socioeconomic groups.
Relative inequalities in all-cause mortality generally
increased, but much more so in Hungary and Norway than
in the other four countries. Absolute inequalities by edu-
cation often narrowed, particularly among men, but
widened in Hungary and Norway. Over time, cardiovas-
cular diseases lost their role as main contributor to
inequalities in all-cause mortality in Finland, England and
Wales, and Norway. As a result of these trends, Hungary
(where inequalities in mortality were almost absent in the
1970s) and Norway (where inequalities in the 1970s among
men were amongst the smallest of the six countries in this
study) in the most recent period had larger inequalities in
mortality than the other four countries.
Interpretation
It is only over a period of several decades that one can
clearly see the very dynamic nature of trends in inequalities
in mortality, with some countries that originally had small
inequalities in mortality (Hungary and Norway) rising to
the top, while others made considerable progress in
reducing inequalities in mortality, at least as measured on
an absolute scale.
To the best of our knowledge, this is the first study on
long-term trends in inequalities in mortality that includes
Hungary. Our analysis shows that, contrary perhaps to what
might have been expected, the widening of inequalities in
mortality already started during the 1980s, i.e., before the
fall of the communist regime in 1989 (Kovacs 2014). In the
1970s and 1980s, relative inequalities in mortality in
Hungary were among the smallest observed in the coun-
tries included in our analysis, but in the early 1990s they
were already larger than elsewhere among men, and in the
early 2000s were also larger than elsewhere among women
(Fig. 1). This explosion of inequalities is due to declining
mortality among the high educated at a time when mor-
tality among the low educated was still rising (Tables 2, 3).
The early start of these developments suggests that its
explanation should not be sought exclusively, or even
primarily, in the political and economic changes occurring
after the fall of the Soviet Union, although these may have
exacerbated the already on-going deterioration of the
health status of the low educated (Leinsalu et al. 2009). The
fact that cardiovascular disease mortality among the high
educated in Hungary already started to decline during the
1980s (web appendix Table 3) suggests that favorable
changes in cardiovascular risk factors (e.g., dietary
changes, or smoking cessation) and/or treatment of car-
diovascular disease had by then already reached the high
educated in Hungary (Kovacs 2014). Figure 2b shows that
the ‘reverse’ inequalities in all-cause mortality seen among
Hungarian women in the early 1970s and early 1980s were
primarily due to higher cancer mortality among the high
educated. Cancers for which mortality was higher among
the high educated include those of breast and lung (results
not shown), suggesting that ‘modern’ child-bearing and
smoking behaviors had already been adopted by high but
not by low educated women in Hungary in these years
(Strand et al. 2007).
It is perhaps no surprise that Hungary, with its different
political history, appears to inhabit a different world in
terms of health inequalities, but there is also a stark con-
trast between Norway and the other Western- and Southern
European countries. Relative inequalities in Norway have
steeply risen, and absolute inequalities have increased as
well, at least until the first half of the 2000s. This is partly
related to a rise in inequalities in mortality due to cardio-
vascular diseases until the late 1980s among men, and a
more recent rise in inequalities in mortality due to cancer
and other diseases among both men and women (Fig. 2).
Inequalities in mortality from lung cancer have risen sub-
stantially in Norway among both men and women, while
they are already declining among men in other Northern
and Western European countries, suggesting that a delayed
smoking epidemic partly explains Norway’s unfavorable
trajectory (Gregoraci et al. 2016, accepted for publication).
The fact that inequalities in cardiovascular disease are
already declining in Norway, whereas inequalities in lung
cancer are still increasing, despite the fact that both dis-
eases are related to smoking, can probably be explained by
differences in the lag-time between smoking and the
occurrence of these two diseases.
Relative inequalities among French men and women and
Italian women also stood out because they remained stable,
partly due to strong declines in mortality from other dis-
eases and external causes among the low educated.
Excessive alcohol consumption plays a role in mortality
from both other diseases (e.g. in the form of liver cirrhosis)
and external causes, and as we have shown elsewhere,
while inequalities in alcohol-related mortality have
increased in many countries in Northern, Western and
Central/Eastern Europe, they have not in Southern Europe,
possibly because of between-country variations in alcohol
control policies and in changes in the affordability of
alcohol (Mackenbach et al. 2015).
The secular change of declining rates of cardiovascular
disease mortality has been hailed as opening a new stage in
the epidemiologic transition (Olshansky and Ault 1986),
but also as one of the factors underlying the widening of
inequalities in mortality in recent decades (Marmot and
138 R. de Gelder et al.
123
McDowall 1986). Widening of inequalities in mortality has
even been proposed as an indicator of the start of a new
stage in the epidemiologic transition (Vallin and Mesle
2004). However, our results show that whether or not one
sees a widening occurring depends strongly on the per-
spective chosen. Absolute inequalities in cardiovascular
disease mortality have often declined, because mortality
decline was stronger in absolute terms among the low than
among the high educated. Declines in mortality from
ischemic heart disease and cerebrovascular disease over the
past decades in high-income countries have been attributed
to a combination of risk factor changes (e.g. changes in
smoking, blood pressure, cholesterol, physical activity) and
improvements in treatment of these conditions (e.g.,
revascularization, secondary prevention) (Hunink et al.
1997; McGovern et al. 1992; Sarti et al. 2000; Unal et al.
2004). Larger absolute declines in mortality from cardio-
vascular disease among the low than the high educated
indicate that these developments have also considerably
benefitted the low educated, as has indeed been demon-
strated empirically for England (Bajekal et al. 2012;
Scholes et al. 2012). This suggests that reach of prevention
programs and access to treatment among lower socioeco-
nomic groups are crucial for reducing inequalities in
mortality. Nevertheless, our data also show that relative
inequalities in cardiovascular mortality have increased
rapidly, due to larger percentage reductions in cardiovas-
cular mortality among the high than the low educated.
Therefore, inequalities in cardiovascular disease mortality
remain a reason for concern, although they now no longer
stand out as the most important contributor to inequalities
in all-cause mortality in Northern and Western Europe.
Strengths and limitations
This is the first study of long-term trends in socioeconomic
inequalities in mortality covering 4 decades and 6 Euro-
pean countries. Such a broad coverage naturally also comes
with certain limitations. For example, in Italy only data
from the city of Turin could be included. However, recent
national-level studies from Italy (Federico et al. 2013;
Marinacci et al. 2013) found inequalities that were similar
to those that were observed in this study, which suggests
that Turin does not misrepresent the whole country.
Questions may also be raised on the between- and
within-country comparability of the data. For instance,
variations between and within countries in the certification
and coding of causes of death may have biased our results.
However, this should not be a substantial problem in our
study, because misclassification is most likely to happen
within, and not between, the broad categories of causes of
death analyzed here. Potentially more important are dif-
ferences and changes in the classification of educational
level. Most countries provided longitudinal linked data, in
which educational information for both the numerator and
denominator of mortality rates came from the census, but
Hungary had unlinked cross-sectional data only. In coun-
tries with cross-sectional data numerator/denominator bias
can occur, due to the fact that relatives of a deceased
sometimes give incorrect information on the educational
level of that person. As a consequence, educational dif-
ferences in mortality could be under- or overestimated in
Hungary (Shkolnikov et al. 2012). However, the type of
data did not change over time, and therefore the within-
country trend analysis, which was the main aim of our
study, is unlikely to be biased.
Educational systems and classifications may also have
changed over time. We reclassified national educational
levels into the ISCED scheme (UNESCO 2006), and then
re-categorized these categories into 3 broader educational
levels, which should have removed most differences in
classification within and between countries. However,
categorizing education into ‘low’, ‘middle’ and ‘high’ level
entailed some difficulties. In England and Wales, for
instance, ‘middle’ level education had been grouped with
‘low’ education during the 1980s and 1990s. For England
and Wales, RIIs and SIIs could only be calculated for the
1970s and 2000s (for which 3 education groups were
originally available), and inequality trends between these
periods were estimated based on RIIs/SIIs in 1975–1979
and 2000–2004. As a sensitivity analysis, inequalities were
also measured as mortality rate ratio and rate difference
between those with low and high education for all time
periods, which yielded similar results (web appendix fig-
ure 2a and b).
Furthermore, in Hungary, vocational education was
categorized as ‘low’ instead of ‘middle’ level education
(which is recommended by ISCED guidelines), because it
was part of compulsory education before the Hungarian
educational system was reformed in the 1970s. As a con-
sequence, we likely have underestimated inequalities in
Hungary during the whole study period to some extent.
Both education and occupational class were included as
indicators of socioeconomic position. Even though results
were to some extent similar, discrepancies between the two
measures were also observed: in Finland, England and
Wales and Italy (Turin) absolute inequalities by education
went down, whereas those by occupational class were
largely stable or increased. These discrepancies may be due
to problems in classification, particularly for occupational
class (e.g., due to the exclusion of economically inactive),
or to differences in the age group that was analyzed (35–79
for education, 35–64 for occupation), but there may also be
substantive explanations, e.g., in terms of differences
between the two indicators in capturing various aspects of
socioeconomic position. Unfortunately, no data on
Long-term trends of inequalities in mortality in 6 European countries 139
123
mortality by occupational class were available for Norway
and Hungary, the two countries with the most unfavorable
trends in educational inequalities.
Our analysis covered the age group 35–79 years at death
(35–64 for analyses by occupation), and thereby excludes
younger age groups, and also under-represents older age
groups which currently carry the largest share of the burden
of mortality, particularly in Western Europe where average
life expectancy at birth in the 2000s exceeded 75 years for
men and 80 years for women. This implies that our results
cannot necessarily be generalized to mortality in the whole
population. The slightly older population in Norway (aged
40–79 at death) may have resulted in somewhat higher
absolute inequalities, but are unlikely to have affected the
observed trends of inequalities.
Conclusions
There is no agreement among researchers or policy-makers
on whether to use relative or absolute measures for moni-
toring progress towards reduction of health inequalities
(Mackenbach and Kunst 1997; Harper et al. 2010). We
tend to think that while both are important, in the end
absolute inequalities matter most, because ultimately it is
the absolute excess death rate in lower socioeconomic
groups that affects people’s lives, not the relative excess of
a more and more infrequent event (Mackenbach 2015). It is
therefore very good news that absolute inequalities are
narrowing over time in several European countries.
Over this 40-year period, trends in inequalities in mor-
tality have been very dynamic, with large variations
between countries. This shows that inequalities in mortality
are not immutable, and that while some countries have
experienced dramatic setbacks, others have made sub-
stantial progress in reducing inequalities in mortality. The
diminishing role of cardiovascular disease in generating
inequalities in all-cause mortality further suggests that
policies to reduce inequalities in mortality should start to
focus more on other diseases, including cancer and alcohol-
related conditions.
Acknowledgements We thank the Office of National Statistics
(Newport (Wales)) for providing data on England and Wales, and
Caspar Looman (Department of Public Health, Erasmus MC, Rot-
terdam, Netherlands) for providing statistical support. This study was
supported by a Grant (FP7-CP-FP Grant No. 278511) from the
European Commission Research and Innovation Directorate General,
as part of the ‘‘Developing methodologies to reduce inequalities in the
determinants of health’’ (DEMETRIQ) project.
Compliance with ethical standards
Ethical approval This article is based on a secondary analysis of
administrative data, and does not contain any studies with human
participants performed by any of the authors. Obtaining informed
consent or approval by a medical ethics board was not required under
national regulations.
Funding This study was supported by a grant (FP7-CP-FP Grant No.
278511) from the European Commission Research and Innovation
Directorate General.
Conflict of interest Prof. Mackenbach, Prof. Martikainen and Prof.
Costa received a grant for performing this study from the European
Commission. Otherwise, all authors declare to have no conflicts of
interest.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
made.
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